Environment factor control device and training method thereof

ABSTRACT

Disclosed is a non-transitory computer readable medium storing a computer program, wherein the computer program includes instructions to perform following steps for data processing when the computer program is executed by one or more processors, the steps including: recognizing at least one continuous section from each raw data subset; determining at least one serialization point, based on a start point and an end point of each of the at least one continuous section for each of the raw data subset; and generating a training data set by generating serialized training data, based on the at least one serialization point.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.17/238,105, filed Apr. 22, 2021, which is continuation of InternationalApplication No. PCT/KR2021/001512, filed Feb. 5, 2021, which claimspriority to and the benefit of Korean Patent Application No.10-2020-0049902 filed in the Korean Intellectual Property Office on Apr.24, 2020, the entire contents of which applications are incorporatedherein by reference.

BACKGROUND Technical Field

The present disclosure relates to an apparatus for controlling anenvironmental factor by using a computer device, and a training methodthereof, and more particularly, to a method of transforming trainingdata having a discontinuous form to a continuous form.

Description of the Related Art

In general, in an apparatus, such as a microorganism cultivationapparatus, requiring 24-hour monitoring and environmental factorcontrol, remote control has been performed by a human operator in therelated art.

The control method in the related art has an advantage of beingperformed based on the expertise of the human operator in the field, buthas a disadvantage in that real-time control of the corresponding deviceis impossible.

Accordingly, there are demands in the art for an apparatus capable ofappropriately controlling an environmental factor in real time based onan operation pattern of a human operator and a control method using theapparatus.

Prior art literatures of the present disclosure are as follows.

-   (Patent Document 1) Korean Patent No. 10-1129723-   (Patent Document 2) US Patent Application Publication No.    2012/0054131-   (Patent Document 3) US Patent Application Publication No.    2019/0250203

BRIEF SUMMARY

The present disclosure is conceived to respond to the foregoingbackground art, and provides a method of training an environmentalfactor control automation model.

The technical benefits of the present disclosure are not limited to theforegoing technical benefits, and other non-mentioned technical benefitswill be clearly understood by those skilled in the art from thedescription below.

According to an embodiment of the present disclosure for solving theforegoing problems, a non-transitory computer readable medium storing acomputer program is disclosed. The computer program includesinstructions to perform following steps for data processing when thecomputer program is executed by one or more processors, the stepsincluding: recognizing at least one continuous section from each rawdata subset; determining at least one serialization point, based on astart point and an end point of each of the at least one continuoussection for each of the raw data subset; and generating training data bygenerating serialized training data, based on the at least oneserialization point.

The raw data subset may be in a form of a step function.

The serialization point may be determined based on a start point and anend point of the continuous section and a predetermined ratio of lengthof the continuous section.

The predetermined ratio may differ depending on a type of the raw data.

The serialization point may exceed two within the continuous section.

The serialization point may be determined based on a start point and anend point of two or more subsections for each of the two or moresubsections separating the continuous section.

Each of the two or more subsections may be different in length.

The generating of serialized training data may be comprised of:determining a plurality of points connecting the at least oneserialization point through an interpolation method.

The plurality of points may be determined by interpolating with a linearfunctional or a multi-order function.

The interpolation method may be at least one of a linear interpolationor a spline interpolation.

The non-transitory computer readable medium may further include:training an environmental factor control automation model by using thetraining data set; evaluating performance of the environmental factorcontrol automation model; and determining whether to generate a newtraining data set by resetting the serialization point, based on aresult of performance evaluating.

The performance may be measured based on Mean Square Error (MSE) ofValidation data set.

According to another embodiment of the present disclosure for solvingthe foregoing problems, an apparatus for environmental factor controlautomation is disclosed. The apparatus for environmental factor controlautomation includes: a memory; and a processor, in which the processoris configured to: recognize at least one continuous section from eachraw data subset; determine at least one serialization point, based on astart point and an end point of each of the at least one continuoussection for each of the raw data subset; and generate training data bygenerating serialized training data, based on the at least oneserialization point.

According to still another embodiment of the present disclosure forsolving the foregoing problems, a non-transitory computer-readablemedium storing data structure is disclosed. The non-transitorycomputer-readable medium storing data structure is a non-transitorycomputer-readable medium storing data structure corresponding to aparameter of neural network where at least a part is updated during atraining process, wherein an operation of the neural network is based onat least a part of the parameter, the training process including:recognizing at least one continuous section from each raw data subset;determining at least one serialization point, based on a start point andan end point of each of the at least one continuous section for each ofthe raw data subset; and generating a training data set by generatingserialized training data, based on the at least one serialization point.

The present disclosure may provide a method of training an environmentalfactor control apparatus automation model.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a computing device for performingan environmental factor control automation method according to anembodiment of the present disclosure.

FIG. 2 is a schematic diagram illustrating a network function forperforming the environmental factor control automation method accordingto an embodiment of the present disclosure.

FIG. 3 is a diagram illustrating an example of a recurrent neuralnetwork that is one form of a network function according to the presentdisclosure.

FIG. 4 is a flowchart illustrating an example of a method of generating,by a processor, an environmental factor control automation modelaccording to the present disclosure.

FIG. 5 is a diagram illustrating an example of a determination of aserialization point by the processor according to the presentdisclosure.

FIGS. 6A and 6B are diagrams illustrating an example of training data onwhich interpolation is performed by the processor according to thepresent disclosure.

FIG. 7 is a flowchart illustrating an example of a method of generatingthe environmental factor control automation model by the processoraccording to the present disclosure.

FIG. 8 is a simple and general schematic diagram for an example of acomputing environment in which embodiments of the present disclosure areimplementable.

DETAILED DESCRIPTION

Various embodiments are described with reference to the drawings. In thepresent specification, various descriptions are presented forunderstanding the present disclosure. However, it is obvious that theembodiments may be carried out even without a particular description.

Terms, “component,” “module,” “system,” and the like used in the presentspecification indicate a computer-related entity, hardware, firmware,software, a combination of software and hardware, or execution ofsoftware. For example, a component may be a procedure executed in aprocessor, a processor, an object, an execution thread, a program,and/or a computer, but is not limited thereto. For example, both anapplication executed in a computing device and a computing device may becomponents. One or more components may reside within a processor and/oran execution thread. One component may be localized within one computer.One component may be distributed between two or more computers. Further,the components may be executed by various computer readable media havingvarious data structures stored therein. For example, components maycommunicate through local and/or remote processing according to a signal(for example, data transmitted to another system through a network, suchas the Internet, through data and/or a signal from one componentinteracting with another component in a local system and a distributedsystem) having one or more data packets.

A term “or” intends to mean comprehensive “or” not exclusive “or.” Thatis, unless otherwise specified or when it is unclear in context, “X usesA or B” intends to mean one of the natural comprehensive substitutions.That is, when X uses A, X uses B, or X uses both A and B, “X uses A orB” may be applied to any one among the cases. Further, a term “and/or”used in the present specification shall be understood to designate andinclude all of the possible combinations of one or more items among thelisted relevant items.

A term “include” and/or “including” shall be understood as meaning thata corresponding characteristic and/or a constituent element exists.However, a term “include” and/or “including” means that a correspondingcharacteristic and/or a constituent element exists, but it shall beunderstood that the existence or an addition of one or more othercharacteristics, constituent elements, and/or a group thereof is notexcluded. Further, unless otherwise specified or when it is unclear thata single form is indicated in context, the singular shall be construedto generally mean “one or more” in the present specification and theclaims.

The term “at least one of A and B” should be interpreted to mean “thecase including only A,” “the case including only B,” and “the case whereA and B are combined.”

Those skilled in the art shall recognize that the various illustrativelogical blocks, configurations, modules, circuits, means, logic, andalgorithm operations described in relation to the embodimentsadditionally disclosed herein may be implemented by electronic hardware,computer software, or in a combination of electronic hardware andcomputer software. In order to clearly exemplify interchangeability ofhardware and software, the various illustrative components, blocks,configurations, means, logic, modules, circuits, and operations havebeen generally described above in the functional aspects thereof.Whether the functionality is implemented as hardware or software dependson a specific application or design restraints given to the generalsystem. Those skilled in the art may implement the functionalitydescribed by various methods for each of the specific applications.However, it shall not be construed that the determinations of theimplementation deviate from the range of the contents of the presentdisclosure.

The description about the presented embodiments is provided so as forthose skilled in the art to use or carry out the present disclosure.Various modifications of the embodiments will be apparent to thoseskilled in the art. General principles defined herein may be applied toother embodiments without departing from the scope of the presentdisclosure. Therefore, the present disclosure is not limited to theembodiments presented herein. The present disclosure shall beinterpreted within the broadest meaning range consistent to theprinciples and new characteristics presented herein.

FIG. 1 is a block diagram illustrating an example of a computing devicefor performing an environmental factor control automation methodaccording to the present disclosure.

The configuration of a computing device 100 illustrated in FIG. 1 ismerely a simplified example. In the embodiment of the presentdisclosure, the computing device 100 may include other configurationsfor performing a computing environment of the computing device 100, andonly some of the disclosed configurations may also configure thecomputing device 100.

The computing device 100 may include a processor 110 and a memory 120.

The processor 110 may be formed of one or more cores, and may include aprocessor, such as a central processing unit (CPU), a general-purposegraphics processing unit (GPGPU), and a tensor processing unit (TPU) ofthe computing device, for performing a data analysis and deep learning.The processor 110 may read a computer program stored in the memory 120and process data for machine learning according to an embodiment of thepresent disclosure. According to the embodiment of the presentdisclosure, the processor 110 may perform computation for training aneural network. The processor 110 may perform a calculation, such asprocessing of input data for training in Deep Learning (DN), extractionof a feature from input data, an error calculation, and updating of aweight of the neural network by using backpropagation, for training theneural network. At least one of the CPU, GPGPU, and TPU of the processor110 may process training of a network function. For example, the CPU andthe GPGPU may process training of the network function and dataclassification by using a network function together. Further, in theembodiment of the present disclosure, the training of the networkfunction and the data classification by using a network function may beprocessed by using the processors of the plurality of computing devicestogether. Further, the computer program executed in the computing deviceaccording to the embodiment of the present disclosure may be a CPU,GPGPU, or TPU executable program.

According to the embodiment of the present disclosure, the memory 120may store a predetermined type of information generated or determined bythe processor 110 and a predetermined type of information received by anetwork unit.

According to the embodiment of the present disclosure, the memory 120may include at least one type of storage medium among a flash memorytype, a hard disk type, a multimedia card micro type, a card type ofmemory (for example, an SD or XD memory), a Random Access Memory (RAM),a Static Random Access Memory (SRAM), a Read-Only Memory (ROM), anElectrically Erasable Programmable Read-Only Memory (EEPROM), aProgrammable Read-Only Memory (PROM), a magnetic memory, a magneticdisk, and an optical disk.

FIG. 2 is a schematic diagram illustrating a network function forperforming the environmental factor control automation method accordingto an embodiment of the present disclosure.

Throughout the present specification, a computation model, a nervenetwork, the network function, and the neural network may be used withthe same meaning. The neural network may be formed of a set ofinterconnected calculation units which are generally referred to as“nodes.” The “nodes” may also be called “neurons.” The neural networkconsists of one or more nodes. The nodes (or neurons) configuring theneural network may be interconnected by one or more links.

In the neural network, one or more nodes connected through the links mayrelatively form a relationship of an input node and an output node. Theconcept of the input node is relative to the concept of the output node,and a predetermined node having an output node relationship with respectto one node may have an input node relationship in a relationship withanother node, and a reverse relationship is also available. As describedabove, the relationship between the input node and the output node maybe generated based on the link. One or more output nodes may beconnected to one input node through a link, and a reverse case may alsobe valid.

In the relationship between an input node and an output node connectedthrough one link, a value of the output node may be determined based ondata input to the input node. Herein, a node connecting the input nodeand the output node may have a weight. The weight is variable, and inorder for the neural network to perform a desired function, the weightmay be varied by a user or an algorithm. For example, when one or moreinput nodes are connected to one output node by links, respectively, avalue of the output node may be determined based on values input to theinput nodes connected to the output node and weights set in the linkcorresponding to each of the input nodes.

As described above, in the neural network, one or more nodes areconnected with each other through one or more links to form arelationship of an input node and an output node in the neural network.A characteristic of the neural network may be determined according tothe number of nodes and links and a correlation between the nodes andthe links in the neural network, and a value of the weight assigned toeach of the links. For example, when there are two neural networks inwhich the numbers of nodes and links are the same and the weightsbetween the links are different, the two neural networks may berecognized to be different from each other.

The neural network may consist of one or more nodes. Some of the nodesconfiguring the neural network may form one layer based on distancesfrom an initial input node. For example, a set of nodes having adistance of n from an initial input node may form n layers. The distancefrom the initial input node may be defined by the minimum number oflinks, which, in some embodiments, need to be passed from the initialinput node to a corresponding node. However, the definition of the layeris arbitrary for the description, and a degree of the layer in theneural network may be defined by a different method from the foregoingmethod. For example, the layers of the nodes may be defined by adistance from a final output node.

The initial input node may mean one or more nodes to which data isdirectly input without passing through a link in a relationship withother nodes among the nodes in the neural network. Otherwise, the finalinput node may mean one or more nodes that do not include output nodesin a relationship with other nodes in the nodes in the neural network.Otherwise, the initial input node may mean nodes which are not includedin other input nodes connected through the links in a relationshipbetween the nodes based on the link in the neural network. Further, thehidden node may mean nodes configuring the neural network, not theinitial input node and the final output node. In the neural networkaccording to the embodiment of the present disclosure, the number ofnodes of the input layer may be the same as the number of nodes of theoutput layer, and the neural network may be in the form that the numberof nodes decreases and then increases again from the input layer to thehidden layer. Further, in the neural network according to anotherembodiment of the present disclosure, the number of nodes of the inputlayer may be smaller than the number of nodes of the output layer, andthe neural network may be in the form that the number of nodes decreasesfrom the input layer to the hidden layer. Further, in the neural networkaccording to another embodiment of the present disclosure, the number ofnodes of the input layer may be larger than the number of nodes of theoutput layer, and the neural network may be in the form that the numberof nodes increases from the input layer to the hidden layer. The neuralnetwork according to another embodiment of the present disclosure may bethe neural network in the form in which the foregoing neural networksare combined.

A deep neural network (DNN) may mean the neural network including aplurality of hidden layers, in addition to an input layer and an outputlayer. When the DNN is used, it is possible to recognize a latentstructure of data. That is, it is possible to recognize the latentstructures of pictures, texts, videos, voices, and music (for example,an object included in the picture, the contents and the emotion of thetext, and the contents and the emotion of the voice). The DNN mayinclude a convolutional neural network (CNN), a recurrent neural network(RNN), an auto encoder, Generative Adversarial Networks (GAN), arestricted Boltzmann machine (RBM), a deep belief network (DBN), a Qnetwork, a U network, Siamese network, and the like. The foregoingdescription of the deep neural network is merely illustrative, and thepresent disclosure is not limited thereto.

In the embodiment of the present disclosure, the network function mayinclude an auto encoder. The auto encoder may be one type of artificialneural network for outputting output data similar to input data. Theauto encoder may include at least one hidden layer, and the odd-numberedhidden layers may be disposed between the input/output layers. Thenumber of nodes of each layer may decrease from the number of nodes ofthe input layer to an intermediate layer called a bottleneck layer(encoding), and then be expanded symmetrically with the decrease fromthe bottleneck layer to the output layer (symmetric with the inputlayer). In this case, in the example of FIG. 2, it is illustrated thatthe dimension reduction layer and the dimension restoration layer aresymmetrical, but the present disclosure is not limited thereto, and thenodes of the dimension reduction layer and the dimension restorationlayer may be symmetrical and may be asymmetrical. The auto encoder mayperform a nonlinear dimension reduction. The number of input layers andthe number of output layers may correspond to the number of sensors leftafter preprocessing of the input data. In the auto encoder structure,the number of nodes of the hidden layer included in the encoderdecreases as a distance from the input layer increases. When the numberof nodes of the bottleneck layer (the layer having the smallest numberof nodes located between the encoder and the decoder) is too small, thesufficient amount of information may not be transmitted, so that thenumber of nodes of the bottleneck layer may be maintained in a specificnumber or more (for example, a half or more of the number of nodes ofthe input layer and the like).

The neural network may be learned by at least one scheme of supervisedlearning, unsupervised learning, and semi-supervised learning. Thelearning of the neural network is for the purpose of reducing orminimizing an error of an output. In the training of the neural network,training data is repeatedly input to the neural network and an error ofan output of the neural network for the training data and a target iscalculated, and the error of the neural network is back-propagated in adirection from an output layer to an input layer of the neural networkin order to decrease the error, and a weight of each node of the neuralnetwork is updated. In the case of the supervised learning, trainingdata labelled with a correct answer (that is, labelled training data) isused, in each training data, and in the case of the unsupervisedlearning, a correct answer may not be labelled to each training data.That is, for example, the training data in the supervised learning fordata classification may be data, in which category is labelled to eachof the training data. The labelled training data is input to the neuralnetwork and the output (category) of the neural network is compared withthe label of the training data to calculate an error. For anotherexample, in the case of the unsupervised learning related to the dataclassification, training data that is the input is compared with anoutput of the neural network, so that an error may be calculated. Thecalculated error is back-propagated in a reverse direction (that is, thedirection from the output layer to the input layer) in the neuralnetwork, and a connection weight of each of the nodes of the layers ofthe neural network may be updated according to the backpropagation. Avariation rate of the updated connection weight of each node may bedetermined according to a learning rate. The calculation of the neuralnetwork for the input data and the backpropagation of the error mayconfigure a learning epoch. The learning rate is differently applicableaccording to the number of times of repetition of the learning epoch ofthe neural network. For example, at the initial stage of the learning ofthe neural network, a high learning rate is used to make the neuralnetwork rapidly secure performance of a predetermined level and improveefficiency, and at the latter stage of the learning, a low learning rateis used to improve accuracy.

In the learning of the neural network, the training data may begenerally a subset of actual data (that is, data to be processed byusing the learned neural network), and thus an error for the trainingdata is decreased, but there may exist a learning epoch, in which anerror for the actual data is increased. Overfitting is a phenomenon, inwhich the neural network excessively learns training data, so that anerror for actual data is increased. For example, a phenomenon, in whichthe neural network learning a cat while seeing a yellow cat cannotrecognize cats, other than a yellow cat, as cats, is a sort ofoverfitting. Overfitting may act as a reason of increasing an error of amachine learning algorithm. In order to prevent overfitting, variousoptimizing methods may be used. In order to prevent overfitting, amethod of increasing training data, a regularization method, a dropoutmethod of omitting a part of nodes of the network during the learningprocess, and the like may be applied.

FIG. 3 is a diagram illustrating an example of a recurrent neuralnetwork that is a form of an artificial neural network according to thepresent disclosure.

As illustrated in FIG. 3, in the present disclosure, the networkfunction may have a form of a Recurrent Neural Network (RNN), as well asa form of a general artificial neural network. The RNN has acharacteristic in which a connection between units has a recurrentstructure. The structure makes it possible to store a state inside aneural network so that time-varying dynamic feature may be modeled.Unlike a feedforward neural network, the RNN may process a sequence typeof input by using an internal memory. Accordingly, the RNN may processdata having a time-varying feature, such as handwriting recognition andspeech recognition. The foregoing description of the data is merely anexample, and the present disclosure is not limited thereto.

Input data according to the present disclosure is the data input to theneural network, and particularly, when the neural network is the RNN,the input data may be the data for an environmental factor.

Output data according to the present disclosure is a result of inputdata derived through a network function, and may be a value of anenvironmental factor derived by the network function at a current timepoint.

For example, when the method of controlling the environmental factoraccording to the present disclosure relates to a microorganismcultivation apparatus, input data X may include temperature, humidity,oxygen concentration, and the like at the time point. Further, forexample, when the method of controlling the environmental factoraccording to the present disclosure relates to a microorganismcultivation apparatus, output data Y may include rotations per minute(RPM), airflow, and the like of the microorganism cultivation apparatusat the current time point, which are derived by the neural network.

However, the input data and the output data are merely examples, and thetypes of the input data and the output data are not limited thereto.

In the method of controlling the environmental factor according to thepresent disclosure, for the constructed data, the RNN may be trained toderive the value of the environmental factor according to time.

The RNN is generally suitable for modelling sequence/time-series data.Accordingly, the output data Y may be related to the environment factorthat is an object to be controlled according to time. This is merely anexample of the form of the sequence/time-series data, and the type ofthe sequence/time-series data is not limited thereto.

The foregoing content is merely the example of the forms of the inputdata and the output data, so that the input data and the output data arenot limited to the foregoing example.

FIG. 4 is a flowchart illustrating an example of a method of generating,by a processor, an environmental factor control automation modelaccording to the present disclosure.

In the present disclosure, the environment may mean an environment inexperiments, processes, storage, and the like that are continuouslyperformed for a certain period of time. That is, in the example, theenvironment may be a laboratory in which an experiment is conducted, amedium, a sealed space, a freezer that stores arbitrary items, and afurnace in a steel mill, and the like.

In this case, the environmental factor that is the target to becontrolled in the apparatus and the method according to the presentdisclosure may be an RPM of an experimental device, airflow in anexperimental medium controlled based on temperature, humidity, and thelike of an experimental space.

The kind of the environmental factor is merely an example for helpingunderstanding of the object to be controlled of the apparatus and themethod according to the present disclosure, so that the type of thefactor to be controlled should not be limited thereto.

Referring to FIG. 4, the processor 110 may recognize at least onecontinuous section from each raw data subset included in a raw data set(S100).

The raw data subset according to the present disclosure may be a set ofraw data which is a vector having an environmental factor and time aselements.

Particularly, the raw data subset may be sequence data according to timeof the environmental factor.

For example, the environmental factor control automation model accordingto the present disclosure may be an apparatus and a method ofcontrolling an RPM and airflow of an experimental device in amicroorganism cultivation process. In this case, the raw data subset maybe a set including vectors representing an RPM according to time of theplurality of cultivation apparatuses.

For example, the raw data subset may consist of {(2000 rpm, 1 second),(2000 rpm, 2 seconds), (2200 rpm, 3 seconds), . . . }.

The raw data according to the present disclosure may be past data thathas performed a control operation on the environmental factor that isthe object to be controlled. For example, the raw data may be datacollected from records on environmental factors. The control operationprogresses at a predetermined time interval, and a value of the controlfactor is uniform between the control operations. As described above,when the raw data subset is expressed in the form of a graph on acoordinate space, the data included in the data subset may show adiscontinuous form, such as a step function, in the coordinate space.

According to the environmental factor control automation methodaccording to the present disclosure, training data, such as the raw datasubset, for the neural network, may be serialized. In the case where theneural networks reflecting time-series information are trained by usingthe discontinuous data according to the general technology, the neuralnetwork may not be smoothly trained. However, when the serializedtraining data is used like the method according to the presentdisclosure, the neural network may be more effectively trained.

The model trained with the continuous data may control the environmentalfactor according to a change in a surrounding environment in real time.Accordingly, it is possible to solve the problem in the related art inthat it is difficult to an operation manager of the experimental deviceto immediately respond to changes in the environment in the cultureprocess, production process, and the like. That is, even though theenvironment appears to be a static state at a glance, since minutechanges are continuously made within the corresponding environment, themethod of controlling the environmental factor according to the presentdisclosure may ensure the optimum control.

When the operation record is serialized and the neural network istrained with the serialized operation record by the environmental factorcontrol automation model according to the present disclosure, it ispossible to respond to the minute change in the environment to becontrolled in real time while reflecting the existing environmentalfactor control method. Accordingly, goals, such as efficiency ofmicroorganism cultivation process or production process, may beachieved.

For the convenience of description, it was described that the raw datais the two-dimensional vector having the value of one environmentalfactor and the time as the elements. However, the raw data is notlimited thereto, and the raw data may be the vector having two or moretypes of environmental factors and the time as the elements. Forexample, the raw data may consist of two or more environmental factorsand the time, such as RPM, airflow, . . . , and time).

Accordingly, each of the raw data included in the raw data subset mayexpress the states of the various environmental factors according totime, so that the raw data and the raw data subset should not be limitedto and interpreted based on the foregoing example.

In the present disclosure, the plurality of data is “continuous” meansthat when the plurality of data is arranged in a coordinate space,points corresponding to the plurality of data are connected in the spacewithout being disconnected.

Accordingly, when the raw data presented in one continuous section arearranged in the coordinate space, the raw data may be connected in thespace without the disconnection of all of the points.

The continuous section according to the present disclosure may mean aset of points in which each of the raw data is continuously expressedwhen the raw data subset is expressed as a graph.

As an example of the raw data according to the present disclosure, theraw data may have the form of a step function. In this case, when eachof the raw data included in the raw data subset is expressed in thecoordinate space, the raw data subset may have the form of the stepfunction having a plurality of continuous sections. Further, when theplurality of continuous sections is included in the raw data subset,each of the continuous section may be discontinuous with respect to eachother.

Accordingly, the processor 110 according to the present disclosure maydetermine at least one continuous section for each raw data subset.

The processor 110 may determine a start point and an end point of thecontinuous section.

In the present disclosure, the start point and the end point may bedefined for each of the continuous sections. That is, the start pointand the end point may mean determined points at which the continuoussection starts and ends by a predetermined rule. The predetermined rulefor determining the start point and the end point will be describedbelow.

For example, for each of the continuous sections included in the rawdata subset, the processor 110 may determine a point closest to theorigin among the points corresponding to the raw data included in thecontinuous section as the start point and the point farthest from theorigin as the end point. Otherwise, when the raw data includes time asone element, the processor 110 may determine the point having thesmallest time value as the start point and the point having the largesttime value as the end point for each continuous section.

The method of determining the start point and the end point is merely anexample, and the method of determining the start point and the end pointis not limited thereto.

The processor 110 may determine at least one serialization point basedon the start point and the end point of each of at least one continuoussection for each of the raw data subsets (S200).

The processor 110 according to the present disclosure may determine aserialization point in order to connect the plurality of continuoussections when the raw data subset includes the plurality of continuoussections.

For example, the processor 110 may determine a point spaced apart fromthe start point of the continuous section by a predetermined distance asa serialization point for the start point, and a point spaced apart fromthe end point of the continuous section by a predetermined distance as asecond serialization point.

Hereinafter, an example of the method of determining the serializationpoint will be described.

In particular, the processor 110 may calculate a distance by apredetermined ratio for a length of the continuous section. Thepredetermined ratio may be different depending on the environment towhich the environment factor control method and apparatus according tothe present disclosure are applied, and the type of environmentalfactor. In one raw data, a different ratio may be applied to thedifferent kind of environment factor.

The processor 110 may determine the serialization point based on thedistance by the predetermined ratio from the start point and the endpoint of the continuous section for each of the plurality of continuoussections, and connect the plurality of serialization sections togenerate serialized training data.

When the serialization point is determined, the processor 110 may makethe serialization point to be spaced apart from the start point and theend point by the distance by the predetermined ratio with respect toeach axis direction in the coordinate space. The method of determiningthe serialization point will be described in detail with reference toFIG. 5.

According to the environmental factor control automation methodaccording to the present disclosure, training data, such as the raw datasubset, for the neural network, may be serialized. In the case where theneural networks reflecting time-series information are trained by usingthe discontinuous data according to the general technology, the neuralnetwork may not be smoothly trained. In the meantime, when theserialized training data is used like the method according to thepresent disclosure, the neural network may be more effectively trained.

The model trained with the continuous data may control the environmentalfactor according to a change in a surrounding environment in real time.Accordingly, it is possible to solve the problem in the related art inthat it is difficult for an operation manager of the experimental deviceto immediately respond to changes in the environment in the cultureprocess, production process, and the like. That is, even though theenvironment appears to be a static state at a glance, since minutechanges are continuously made within the corresponding environment, themethod of controlling the environmental factor according to the presentdisclosure may ensure the optimum control.

When the operation record is serialized and the neural network istrained with the serialized operation record by the environmental factorcontrol automation model according to the present disclosure, it ispossible to respond to the minute change in the environment to becontrolled in real time while reflecting the existing environmentalfactor control method. Accordingly, goals, such as efficiency ofmicroorganism cultivation process or production process, may beachieved.

The processor 110 may generate serialized training data based on atleast one serialization point for each of the raw data subsets (S300).

The processor 110 may generate serialized training data by connectingthe plurality of serialization points determined in operation S200.

Herein, the connection of the plurality of serialization points may meanthe generation of the plurality of points, which connects twoserialization points, in the coordinate space by applying aninterpolation method to a space between the two serialization points.

The interpolation method will be described below in detail withreference to FIG. 6A and FIG. 6B.

The connection of the two serialization points through the interpolationmethod is merely an example of the method of generating the serializedtraining data, and the method of generating the serialized training datais not limited thereto.

The processor 110 may generate an environmental factor controlautomation model by using the generated serialized training data (S400).

The processor 110 may generate the serialized training data for each ofthe raw data subsets. Accordingly, the plurality of serialized trainingdata may be generated for each of the raw data sets.

The processor 110 may generate the environmental factor controlautomation model by training the environmental factor control automationmodel by using the generated serialized training data.

This will be described in detail with reference to FIG. 7.

FIG. 5 is a diagram illustrating an example of the determination of theserialization point by the processor according to the presentdisclosure.

The continuous section according to the present disclosure may mean aset of points in which each of the raw data is continuously expressedwhen the raw data subset is expressed as a graph.

That is, when each of the raw data included in the raw data subset isexpressed in the coordinate space, the continuous points form acontinuous section. When the plurality of continuous sections isincluded in the raw data subset, each of the continuous section may bediscontinuous with respect to each other.

As illustrated in FIG. 5, when the raw data subset is expressed in agraph, the plurality of continuous sections W may be presented.

FIG. 5 illustrates the case where when the raw data included in one rawdata subset is two-dimensionally expressed, the graphs have the form ofa step function. As illustrated in FIG. 5, in this case, the pluralityof continuous sections W may be presented. The plurality of continuoussections W may be discontinuous with respect to each other. That is, onecontinuous section W may be discontinuous with respect to anothercontinuous section W at the start point and the end point of onecontinuous section W.

The processor 110 according to the present disclosure may determine aplurality of serialization points in order to generate serializedtraining data in the case where the plurality of continuous sections isincluded in the raw data subset.

Hereinafter, for convenience of the description, in the case where theraw data subset is two-dimensionally expressible, an example of themethod of determining a serialization point by the processor 110 will bedescribed.

Referring to FIG. 5, the processor 110 may determine a firstserialization interval V and a second serialization interval H for apredetermined continuous section. The processor 110 may determine apoint which is spaced apart from the start point of the continuoussection by the first serialization interval V in a time axis directionand is spaced apart from the start point of the continuous section bythe second serialization interval H in a direction of an axis of thefactor to be controlled as a serialization point 200.

In this case, the first serialization interval V and the secondserialization interval H may be determined based on a length of thecontinuous section W. For example, the first serialization interval Vand the second serialization interval H may be determined with apredetermined ratio of the length of the continuous section W. Further,the first serialization interval V may be the same as or different fromthe second serialization interval H. Further, the first serializationinterval V and the second serialization interval H may be different fromeach other for the serialization point corresponding to the start pointof the continuous section and the serialization point corresponding tothe end point of the continuous section.

For convenience of the description, the method is described based on thecase where the raw data is the two-dimensional vector (the number offactors to be controlled is one), but even when the raw data is thevector exceeding two dimensions, the serialization point may bedetermined as described above. Accordingly, the method of determiningthe serialization point should not be limited to the foregoing contents.

The serialization interval according to the present disclosure may meanthe interval determined with respect to the axis of one direction in thecoordinate space in which the raw data is expressed in order todetermine the serialization point. As described above, the serializationinterval may be determined by using the distance of the continuoussection W.

The serialization intervals V and H may be hyper parameters of theenvironmental factor control automation model according to the presentdisclosure.

The serialization interval may exist as much as the number of dimensionsof the vector in which the raw data is expressed. Accordingly, it is notthat the determination of the serialization point is possible only inthe two-dimensional space as illustrated in FIG. 5.

The serialization point 200 according to the present disclosure mayexceed two within one continuous section.

In particular, the processor 110 may separate one continuous sectioninto two or more subsections in order to generate the serializationpoints exceeding two.

The processor 110 may determine a start point and an end point for eachof the subsection, and determine a serialization point for each of thesubsection based on a length of the subsection, and the start point andthe end point of the subsection.

In this case, the lengths of the plurality of subsections separated fromone continuous section may be different from each other.

When the continuous section is separated into the plurality ofsubsections, more serialization points may be generated. Accordingly,more various types of training data may be generated. Through this, theenvironmental factor control automation model according to the presentdisclosure may be more efficiently trained.

In the present disclosure, the environment may mean an environment inexperiments, processes, storage, and the like that are continuouslyperformed for a certain period of time. That is, in the example, theenvironment may be a laboratory in which an experiment is conducted, amedium, a sealed space, a freezer that stores arbitrary items, and afurnace in a steel mill, and the like.

In this case, the factor to be controlled in the apparatus and themethod according to the present disclosure may be an RPM of anexperimental apparatus, or airflow of a freezer, a furnace, and thelike, which are controlled based on temperature, humidity, and the likeof an experimental space, a freezer, the furnace, and the like.

The foregoing content is merely illustrative for helping understandingof the object to be controlled of the apparatus and the method accordingto the present disclosure, so that the type of factor to be controlledshould not be limited thereto.

FIGS. 6A and 6B are diagrams illustrating an example of training data onwhich interpolation is performed by the processor according to thepresent disclosure.

The interpolation method is the method of constructing a new data pointwithin an isolated point of a known data point.

In engineering and science, there may be numerous data points, which maybe obtained through sampling and experiments, and through this, afunction value for the limited number of values of an independentvariable is expressed.

Accordingly, in the present disclosure, the processor 110 may generatedata points connecting the determined serialization points by using theinterpolation method.

The linear interpolation method is the method of linearly calculatingaccording to the straight distance in order to estimate a value locatedbetween values of the end points when the values of the end points aregiven.

In the present disclosure, the spline interpolation method may mean themethod of dividing an entire section into subsections to obtain a smoothfunction with low-order polynomial fragments.

When the interpolation method is used like the method according to thepresent disclosure, the serialized training data may be generated byusing the small amount of computation. Accordingly, the total amount ofcomputation for training the neural network according to the presentdisclosure may be decreased.

The processor 110 according to the present disclosure may determine aserialization point in order to connect the plurality of continuoussections when the raw data subset includes the plurality of continuoussections.

Hereinafter, for convenience of the description, in the case where theraw data subset is two-dimensionally expressible, an example of themethod of determining a serialization point by the processor 110 will bedescribed.

Referring to FIG. 5, the processor 110 may determine a firstserialization interval V and a second serialization interval H for apredetermined continuous section. The processor 110 may determine apoint which is spaced apart from the start point of the continuoussection by the first serialization interval V in a time axis directionand is spaced apart from the start point of the continuous section bythe second serialization interval H in a direction of an axis of thefactor to be controlled as a serialization point 200.

In this case, the first serialization interval V and the secondserialization interval H may be determined based on a length of thecontinuous section W. For example, the first serialization interval Vand the second serialization interval H may be determined with apredetermined ratio of the length of the continuous section W. Further,the first serialization interval V may be the same as or different fromthe second serialization interval H. Further, the first serializationinterval V and the second serialization interval H may be different fromeach other for the serialization point corresponding to the start pointof the continuous section and the serialization point corresponding tothe end point of the continuous section.

For convenience of the description, the method is described based on thecase where the raw data is the two-dimensional vector (the number offactors to be controlled is one), but even when the raw data is thevector exceeding two dimensions, the serialization point may bedetermined as described above. Accordingly, the method of determiningthe serialization point should not be limited to the foregoing contents.

As illustrated in FIG. 6A and FIG. 6B, the processor 110 may connect thedetermined serialization points 200.

For example, the processor 110 may connect the plurality ofserialization points by using the linear interpolation method. That is,the processor 110 may generate the point connecting the plurality ofserialization points through the linear interpolation method.

For another example, the processor may connect the plurality ofserialization points in a curve form as illustrated in FIG. 6B. In thiscase, the processor 110 may connect the plurality of serializationpoints with polynomials of a second order or higher by using the splineinterpolation method. Through this, the serialized training data in thecurve form may be generated.

By generating the training data by interpolating the serializationpoints by various methods, in addition to the linear interpolationmethod, more various forms of training data may be generated. Throughthis, the environmental factor control automation model according to thepresent disclosure may be more efficiently trained.

FIG. 7 is a flowchart illustrating an example of a method of generatingthe environmental factor control automation model by the processoraccording to the present disclosure.

Referring to FIG. 7, the processor 110 may train the environmentalfactor control automation model by using the serialized training data(S410).

The environmental factor control automation model according to thepresent disclosure may be trained to receive a vector expressing anenvironmental factor and output an appropriate numerical value of thefactor to be controlled. Particularly, when the environmental factorcontrol automation model uses the RNN, the processor 110 may train theenvironmental factor control automation model so that the environmentalfactor control automation model receives raw data for an environmentalfactor up to a previous time point and determine a numerical value of afactor to be controlled at a current time point.

For example, it is assumed that the environmental factor controlapparatus according to the present disclosure is for the purpose ofcontrolling an environmental factor of a microorganism cultivationapparatus. In this case, the processor 110 may train the RNN so that theRNN receives a sequence of input data X of the microorganism cultivationapparatus from a first time point to a (T−1) time point and determineoutput data of the microorganism cultivation apparatus at a T timepoint. As described above, the environmental factor to be controlledherein may be the RPM of the experimental apparatus, and thus, theoutput data may also represent the RPM of the experimental apparatus.However, the output data is merely an example, and the output datashould not be limited thereto.

The processor 110 may evaluate performance of the trained environmentalfactor control automation model (S420).

The processor 110 may evaluate prediction performance for a verificationdata set and a test data set by using the trained environmental factorcontrol automation model.

In particular, the verification data set may include a verification datasubset including the plurality of verification data and the test dataset may include a test data subset including the plurality of test data.

The verification data and the test data provided for evaluating theprediction performance may be the data serialized by the methodsuggested in FIGS. 3 to 6.

In this case, the processor 110 may change the control factor accordingto time through the environmental factor control automation modelaccording to the present disclosure for the verification data set andthe test data set.

The processor 110 may evaluate the performance of the environmentalfactor control automation model by comparing values of the environmentalfactors according to time generated through the model with values of thecontrol factors included in the verification data set and the test dataset.

For example, the processor 110 may evaluate the performance of theenvironmental factor control automation model based on a value of a MeanSquare Error (MSE) for the verification data set.

The processor 110 may generate the plurality of serialized training datahaving different serialization intervals. In this case, the processor110 may determine the serialization interval used for generating theserialized training data having the lowest MSE value as a hyperparameter of the environmental factor control automation model accordingto the present disclosure.

The processor 110 may determine whether a result of the performanceevaluation satisfies a performance reference, and when the result of theperformance evaluation satisfies the performance reference (S430, YES),the processor 110 may terminate the training of the environmental factorcontrol automation model (S450).

When the result of the performance evaluation does not satisfy theperformance reference, the processor 110 may reset the serializationpoint and generate new training data (S440).

The processor 110 may evaluate performance of the environmental factorcontrol automation model by recognizing whether the MSE value of theselected serialized training data is equal to or lower than apredetermined reference.

When the MSE value is equal to or lower than the predeterminedreference, the processor 110 may terminate the training of theenvironmental factor control automation model.

When the result of the performance evaluation does not satisfy theperformance reference, the processor 110 may reset the serializationpoint. In order to reset the serialization point, the processor 110 maydetermine a new serialization interval that is not previously used.

FIG. 8 is a simple and general schematic diagram for an example of acomputing environment in which embodiments of the present disclosure areimplementable.

The present disclosure has been described as being generallyimplementable by the computing device, but those skilled in the art willappreciate well that the present disclosure is combined with computerexecutable commands and/or other program modules executable in one ormore computers and/or be implemented by a combination of hardware andsoftware.

In general, a program module includes a routine, a program, a component,a data structure, and the like performing a specific task orimplementing a specific abstract data form. Further, those skilled inthe art will appreciate well that the method of the present disclosuremay be carried out by a personal computer, a hand-held computing device,a microprocessor-based or programmable home appliance (each of which maybe connected with one or more relevant devices and be operated), andother computer system configurations, as well as a single-processor ormultiprocessor computer system, a mini computer, and a main framecomputer.

The embodiments of the present disclosure may be carried out in adistribution computing environment, in which certain tasks are performedby remote processing devices connected through a communication network.In the distribution computing environment, a program module may belocated in both a local memory storage device and a remote memorystorage device.

The computer generally includes various computer readable media. Thecomputer accessible medium may be any type of computer readable medium,and the computer readable medium includes volatile and non-volatilemedia, transitory and non-transitory media, and portable andnon-portable media. As a non-limited example, the computer readablemedium may include a computer readable storage medium and a computerreadable transmission medium. The computer readable storage mediumincludes volatile and non-volatile media, transitory and non-transitorymedia, and portable and non-portable media constructed by apredetermined method or technology, which stores information, such as acomputer readable command, a data structure, a program module, or otherdata.

The computer readable storage medium includes a Random Access Memory(RAM), a Read Only Memory (ROM), an Electrically Erasable andProgrammable ROM (EEPROM), a flash memory, or other memory technologies,a Compact Disc (CD)-ROM, a Digital Video Disk (DVD), or other opticaldisk storage devices, a magnetic cassette, a magnetic tape, a magneticdisk storage device, or other magnetic storage device, or otherpredetermined media, which are accessible by a computer and are used forstoring desired information, but is not limited thereto.

The computer readable transport medium generally implements a computerreadable command, a data structure, a program module, or other data in amodulated data signal, such as a carrier wave or other transportmechanisms, and includes all of the information transport media. Themodulated data signal means a signal, of which one or more of thecharacteristics are set or changed so as to encode information withinthe signal. As a non-limited example, the computer readable transportmedium includes a wired medium, such as a wired network or adirect-wired connection, and a wireless medium, such as sound, RadioFrequency (RF), infrared rays, and other wireless media. A combinationof the predetermined media among the foregoing media is also included ina range of the computer readable transport medium.

An illustrative environment 1100 including a computer 1102 andimplementing several aspects of the present disclosure is illustrated,and the computer 1102 includes a processing device 1104, a system memory1106, and a system bus 1108. The system bus 1108 connects systemcomponents including the system memory 1106 (not limited) to theprocessing device 1104. The processing device 1104 may be apredetermined processor among various commonly used processors. A dualprocessor and other multi-processor architectures may also be used asthe processing device 1104.

The system bus 1108 may be a predetermined one among several types ofbus structure, which may be additionally connectable to a local bususing a predetermined one among a memory bus, a peripheral device bus,and various common bus architectures. The system memory 1106 includes aROM 1110, and a RAM 1112. A basic input/output system (BIOS) is storedin a non-volatile memory 1110, such as a ROM, an erasable andprogrammable ROM (EPROM), and an EEPROM, and the BIOS includes a basicrouting helping a transport of information among the constituentelements within the computer 1102 at a time, such as starting. The RAM1112 may also include a high-rate RAM, such as a static RAM, for cachingdata.

The computer 1102 also includes an embedded hard disk drive (HDD) 1114(for example, enhanced integrated drive electronics (EIDE) and serialadvanced technology attachment (SATA))—the embedded HDD 1114 beingconfigured for exterior mounted usage within a proper chassis (notillustrated)—a magnetic floppy disk drive (FDD) 1116 (for example, whichis for reading data from a portable diskette 1118 or recording data inthe portable diskette 1118), and an optical disk drive 1120 (forexample, which is for reading a CD-ROM disk 1122, or reading data fromother high-capacity optical media, such as a DVD, or recording data inthe high-capacity optical media). A hard disk drive 1114, a magneticdisk drive 1116, and an optical disk drive 1120 may be connected to asystem bus 1108 by a hard disk drive interface 1124, a magnetic diskdrive interface 1126, and an optical drive interface 1128, respectively.An interface 1124 for implementing an outer mounted drive includes, forexample, at least one of or both a universal serial bus (USB) and theInstitute of Electrical and Electronics Engineers (IEEE) 1394 interfacetechnology.

The drives and the computer readable media associated with the drivesprovide non-volatile storage of data, data structures, computerexecutable commands, and the like. In the case of the computer 1102, thedrive and the medium correspond to the storage of random data in anappropriate digital form. In the description of the computer readablestorage media, the HDD, the portable magnetic disk, and the portableoptical media, such as a CD, or a DVD, are mentioned, but those skilledin the art will well appreciate that other types of computer readablemedia, such as a zip drive, a magnetic cassette, a flash memory card,and a cartridge, may also be used in the illustrative operationenvironment, and the predetermined medium may include computerexecutable commands for performing the methods of the presentdisclosure.

A plurality of program modules including an operation system 1130, oneor more application programs 1132, other program modules 1134, andprogram data 1136 may be stored in the drive and the RAM 1112. Anentirety or a part of the operation system, the application, the module,and/or data may also be cached in the RAM 1112. It will be wellappreciated that the present disclosure may be implemented by severalcommercially usable operation systems or a combination of operationsystems.

A user may input a command and information to the computer 1102 throughone or more wired/wireless input devices, for example, a keyboard 1138and a pointing device, such as a mouse 1140. Other input devices (notillustrated) may be a microphone, an IR remote controller, a joystick, agame pad, a stylus pen, a touch screen, and the like. The foregoing andother input devices are frequently connected to the processing device1104 through an input device interface 1142 connected to the system bus1108, but may be connected by other interfaces, such as a parallel port,an IEEE 1394 serial port, a game port, a USB port, an IR interface, andother interfaces.

A monitor 1144 or other types of display devices are also connected tothe system bus 1108 through an interface, such as a video adaptor 1146.In addition to the monitor 1144, the computer generally includes otherperipheral output devices (not illustrated), such as a speaker and aprinter.

The computer 1102 may be operated in a networked environment by using alogical connection to one or more remote computers, such as remotecomputer(s) 1148, through wired and/or wireless communication. Theremote computer(s) 1148 may be a work station, a computing devicecomputer, a router, a personal computer, a portable computer, amicroprocessor-based entertainment device, a peer device, and othergeneral network nodes, and generally includes some or an entirety of theconstituent elements described for the computer 1102, but only a memorystorage device 1150 is illustrated for simplicity. The illustratedlogical connection includes a wired/wireless connection to a local areanetwork (LAN) 1152 and/or a larger network, for example, a wide areanetwork (WAN) 1154. The LAN and WAN networking environments are generalin an office and a company, and make an enterprise-wide computernetwork, such as an Intranet, easy, and all of the LAN and WANnetworking environments may be connected to a worldwide computernetwork, for example, the Internet.

When the computer 1102 is used in the LAN networking environment, thecomputer 1102 is connected to the local network 1152 through a wiredand/or wireless communication network interface or an adaptor 1156. Theadaptor 1156 may make wired or wireless communication to the LAN 1152easy, and the LAN 1152 also includes a wireless access point installedtherein for the communication with the wireless adaptor 1156. When thecomputer 1102 is used in the WAN networking environment, the computer1102 may include a modem 1158, is connected to a communication computingdevice on a WAN 1154, or includes other means setting communicationthrough the WAN 1154 via the Internet. The modem 1158, which may be anembedded or outer-mounted and wired or wireless device, is connected tothe system bus 1108 through a serial port interface 1142. In thenetworked environment, the program modules described for the computer1102 or some of the program modules may be stored in a remotememory/storage device 1150. The illustrated network connection isillustrative, and those skilled in the art will appreciate well thatother means setting a communication link between the computers may beused.

The computer 1102 performs an operation of communicating with apredetermined wireless device or entity, for example, a printer, ascanner, a desktop and/or portable computer, a portable data assistant(PDA), a communication satellite, predetermined equipment or placerelated to a wirelessly detectable tag, and a telephone, which isdisposed by wireless communication and is operated. The operationincludes a wireless fidelity (Wi-Fi) and Bluetooth wireless technologyat least. Accordingly, the communication may have a pre-definedstructure, such as a network in the related art, or may be simply ad hoccommunication between at least two devices.

The Wi-Fi enables a connection to the Internet and the like even withouta wire. The Wi-Fi is a wireless technology, such as a cellular phone,which enables the device, for example, the computer, to transmit andreceive data indoors and outdoors, that is, in any place within acommunication range of a base station. A Wi-Fi network uses a wirelesstechnology, which is called IEEE 802.11 (a, b, g, etc.) for providing asafe, reliable, and high-rate wireless connection. The Wi-Fi may be usedfor connecting the computer to the computer, the Internet, and the wirednetwork (IEEE 802.3 or Ethernet is used). The Wi-Fi network may beoperated at, for example, a data rate of 11 Mbps (802.11a) or 54 Mbps(802.11b) in an unauthorized 2.4 and 5 GHz wireless band, or may beoperated in a product including both bands (dual bands).

Those skilled in the art may appreciate that information and signals maybe expressed by using predetermined various different technologies andtechniques. For example, data, indications, commands, information,signals, bits, symbols, and chips referable in the foregoing descriptionmay be expressed with voltages, currents, electromagnetic waves,electric fields or particles, optical fields or particles, or apredetermined combination thereof.

Those skilled in the art will appreciate that the various illustrativelogical blocks, modules, processors, means, circuits, and algorithmoperations described in relationship to the embodiments disclosed hereinmay be implemented by electronic hardware (for convenience, called“software” herein), various forms of program or design code, or acombination thereof. In order to clearly describe compatibility of thehardware and the software, various illustrative components, blocks,modules, circuits, and operations are generally illustrated above inrelation to the functions of the hardware and the software. Whether thefunction is implemented as hardware or software depends on design limitsgiven to a specific application or an entire system. Those skilled inthe art may perform the function described by various schemes for eachspecific application, but it shall not be construed that thedeterminations of the performance depart from the scope of the presentdisclosure.

Various embodiments presented herein may be implemented by a method, adevice, or a manufactured article using a standard programming and/orengineering technology. A term “manufactured article” includes acomputer program, a carrier, or a medium accessible from a predeterminedcomputer-readable storage device. For example, the computer-readablestorage medium includes a magnetic storage device (for example, a harddisk, a floppy disk, and a magnetic strip), an optical disk (forexample, a CD and a DVD), a smart card, and a flash memory device (forexample, an EEPROM, a card, a stick, and a key drive), but is notlimited thereto. Further, various storage media presented herein includeone or more devices and/or other machine-readable media for storinginformation. It shall be understood that a specific order or ahierarchical structure of the operations included in the presentedprocesses is an example of illustrative accesses. It shall be understoodthat a specific order or a hierarchical structure of the operationsincluded in the processes may be rearranged within the scope of thepresent disclosure based on design priorities. The accompanying methodclaims provide various operations of elements in a sample order, but itdoes not mean that the claims are limited to the presented specificorder or hierarchical structure.

The description of the presented embodiments is provided so as for thoseskilled in the art to use or carry out the present disclosure. Variousmodifications of the embodiments may be apparent to those skilled in theart, and general principles defined herein may be applied to otherembodiments without departing from the scope of the present disclosure.Accordingly, the present disclosure is not limited to the embodimentssuggested herein, and shall be interpreted within the broadest meaningrange consistent to the principles and new characteristics presentedherein.

As the described above, the relevant contents are described in the bestmode for implementing the present disclosure.

The present disclosure relates to an apparatus for controlling anenvironmental factor by using a computer device, and a training methodthereof, and more particularly, to a method of transforming trainingdata having a discontinuous form to a continuous form.

The various embodiments described above can be combined to providefurther embodiments. All of the U.S. patents, U.S. patent applicationpublications, U.S. patent applications, foreign patents, foreign patentapplications and non-patent publications referred to in thisspecification and/or listed in the Application Data Sheet areincorporated herein by reference, in their entirety. Aspects of theembodiments can be modified, if necessary to employ concepts of thevarious patents, applications and publications to provide yet furtherembodiments.

These and other changes can be made to the embodiments in light of theabove-detailed description. In general, in the following claims, theterms used should not be construed to limit the claims to the specificembodiments disclosed in the specification and the claims, but should beconstrued to include all possible embodiments along with the full scopeof equivalents to which such claims are entitled. Accordingly, theclaims are not limited by the disclosure.

1. A non-transitory computer readable medium storing a computer program,wherein the computer program includes instructions to perform followingsteps for data processing when the computer program is executed by oneor more processors, the steps comprising: generating at least one rawdata subset based on a set of raw data; determining at least onecontinuous section for each of the at least one raw data subset; andgenerating a first training data set by generating a serialized trainingdata, based on each of the at least one continuous section.
 2. Thenon-transitory computer readable medium according to claim 1, whereinthe generating at least one raw data subset based on a set of raw dataincludes: recognizing at least one raw data which is a vector having atleast one type of an environmental factor and time as elements from theset of raw data; and generating the at least one raw data subsetcomprising the at least one raw data recognized.
 3. The non-transitorycomputer readable medium according to claim 1, wherein the at least oneraw data subset includes at least one raw data which is continuouslyexpressed in a coordinate space without disconnection.
 4. Thenon-transitory computer readable medium according to claim 1, whereinthe generating a first training data set by generating a serializedtraining data, based on each of the at least one continuous sectionincludes: determining at least one serialization point based on a startpoint and an end point of each of the at least one continuous section;and generating the serialized training data based on the at least oneserialization point.
 5. The non-transitory computer readable mediumaccording to claim 4, wherein the determining at least one serializationpoint based on a start point and an end point of each of at least onecontinuous section includes: determining a first serialization intervaland a second serialization interval for each of the at least onecontinuous section; and determining a point which is spaced apart fromthe start point by the first serialization interval in a time axisdirection and is spaced apart from the start point by the secondserialization interval in a direction of an axis of environmental factoras a serialization point.
 6. The non-transitory computer readable mediumaccording to claim 5, wherein the determining a first serializationinterval and a second serialization interval for each of the at leastone continuous section includes: determining the first serializationinterval and the second serialization interval based on a length of eachof the at least one continuous section.
 7. The non-transitory computerreadable medium according to claim 5, wherein the determining a firstserialization interval and a second serialization interval for each ofthe at least one continuous section includes: determining the firstserialization interval and the second serialization interval based on apredetermined ratio of a length of each of the at least one continuoussection.
 8. The non-transitory computer readable medium according toclaim 5, wherein the determining a first serialization interval and asecond serialization interval for each of the at least one continuoussection includes: determining the first serialization interval and thesecond serialization interval based on a selected ratio of a length ofeach of the at least one continuous section.
 9. The non-transitorycomputer readable medium according to claim 4, generating the serializedtraining data includes: generating a plurality of data points connectingthe at least one serialization point.
 10. The non-transitory computerreadable medium according to claim 1, further comprising: training anenvironmental factor control automation model by using the firsttraining data set; evaluating performance of the environmental factorcontrol automation model; and determining whether to terminate trainingof the environmental factor control automation model based on a resultof performance evaluating.
 11. The non-transitory computer readablemedium according to claim 10, wherein the training an environmentalfactor control automation model by using the first training data setincludes: training the environmental factor control automation modelbased on the first training data set to derive a value of theenvironmental factor according to time, and wherein the environmentalfactor control automation model is a recurrent neural network.
 12. Thenon-transitory computer readable medium according to claim 10, whereinthe evaluating performance of the environmental factor controlautomation model includes: evaluating performance of the environmentalfactor control automation model by using a verification data set and atest data set.
 13. The non-transitory computer readable medium accordingto claim 12, wherein the evaluating performance of the environmentalfactor control automation model by using a verification data set and atest data set includes: evaluating the performance of the environmentalfactor control automation model by comparing values of environmentalfactors according to time generated by the environmental factor controlautomation model, with values of control factors included in theverification data set and the test data set.
 14. The non-transitorycomputer readable medium according to claim 10, wherein the determiningwhether to terminate training of the environmental factor controlautomation model based on a result of the performance evaluatingincludes: terminating training of the environmental factor controlautomation model when a Mean Square Error (MSE) value of the serializedtraining data is equal to or lower than a predetermined reference. 15.The non-transitory computer readable medium according to claim 10,wherein the determining whether to terminate training of theenvironmental factor control automation model based on a result of theperformance evaluating includes: terminating training of theenvironmental factor control automation model when a MSE value of theserialized training data is equal to or lower than a selected reference.16. The non-transitory computer readable medium according to claim 10,further comprising: determining at least one of a third serializationinterval which differ from a first serialization interval of each of theat least one continuous section, or a fourth serialization intervalwhich differ from a second serialization interval when the result of theperformance evaluating does not satisfy a performance reference; anddetermining to generate a second training data set based on at least oneof the third serialization interval or the fourth serializationinterval.
 17. An apparatus for environmental factor control automation,comprising: a memory; and a processor; wherein the processor isconfigured to: generating at least one raw data subset based on a set ofraw data; determining at least one continuous section for each of the atleast one raw data subset; and generating a first training data set bygenerating a serialized training data, based on each of the at least onecontinuous section.
 18. A non-transitory computer-readable mediumstoring data structure corresponding to a parameter of neural networkwhere at least a part is updated during a training process, wherein anoperation of the neural network is based on at least a part of theparameter, the training process comprising: generating at least one rawdata subset based on a set of raw data; determining at least onecontinuous section for each of the at least one raw data subset; andgenerating a first training data set by generating a serialized trainingdata, based on each of the at least one continuous section.